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AbstractAs greenhouse gas (GHG) emissions from road networks significantly impact the urban environment, there has been a growing interest in integrating environmental factors into traffic controls. Traffic signal control (TSC) is an effective method to reduce air pollution by managing traffic congestion. Accordingly, several recent studies have applied artificial intelligence to TSC to improve traffic efficiency. Despite these efforts, there are a few limitations on the existing studies. First of all, a number of recent studies focus only on traffic performance rather than environmental effects. However, in some cases, the objective function related to traffic efficiency can have an adverse effect on the environment. Furthermore, the variable nature of emissions based on traffic demand is rarely considered. To address these issues, we propose a multi-agent deep reinforcement learning-based TSC to minimize GHGs in traffic networks. To this end, we first identify vehicle’s stop and delay as key factors in traffic efficiency that critically influence air quality. Subsequently, we integrate these factors into our model’s reward function. To validate the proposed model, we conduct experiments under various demand scenarios featuring different traffic volume levels. The results show that the proposed model outperforms comparative control methods in two aspects: traffic performance and environmental sustainability. Especially at a high congestion level, the proposed model reduces average delay time by 11.9–16.1% and increases total throughput by 11.5%. Additionally, it significantly lowers total CO2 emissions by up to 8.9% and average emissions by up to 18.3%. [ABSTRACT FROM AUTHOR]

The acceleration profile of leading vehicles at intersections is critical for emission estimation and microlevel queue simulation. Data obtained from experiments using a high-resolution driving simulator can deliver useful insights into microscale acceleration behaviors at signalized intersections. Acceleration data of the leading vehicles in queues are collected by the simulator. The observed accelerations are found to be stochastic. The acceleration characteristics are also significantly diversified among participants. Hence, a Markov chain is implemented to simulate the acceleration behaviors. The acceleration data are classified into varied operation states. And the Markov chain reconstructs the acceleration profiles of leading vehicles and reproduces the randomness of acceleration behaviors. Among numerous candidate profiles, a speed profile is selected by a proposed criterion that represents the typical acceleration behaviors at signalized intersections. [ABSTRACT FROM AUTHOR]

This paper is an empirical review of the development of two-wheeled transport, comprising human-powered bicycles, E-bikes, and motorbikes in China from 1985 to 2019, aimed at investigating the development of two-wheeled transport in China. A series of demand-side factors (e.g., rapid urbanization) and supply-side factors (e.g., obsolete infrastructure) are explored. The mode share of human-powered bicycles decreased steadily until 2016, the year dockless bikeshare (DBS) emerged. During that time, government policy changed from "encouraged", "discouraged", "converted and re-recognized" to "encouraged again" due to sophisticated socio-economic change. E-bikes and motorbikes witnessed an independent growth trend and changing political attitude from that of human-powered bicycles. The future of the human-powered bicycle transport mode in China is promising, due to favorable government policies and the growing demand from users for a flexible mode of transport to serve short-middle distance and last-mile trips. E-bikes have an uncertain future since local policies differ, and safety issues are yet to be addressed. Motorbikes, due to their negative safety and environmental considerations, have been increasingly replaced by automobiles, and this makes their future position marginal. [ABSTRACT FROM AUTHOR]

• Bike-sharing networks have a small-world property. • The connectivity of bike stations is highly related to their accessibility, while not to their intermediateness. • The spatial distributions of connectivity and accessibility of bike stations have a strong spatial autocorrelation. • Bike-sharing networks have a polycentric structure. To solve the last-mile problem and promote low carbon transportation, hundreds of cities around the world have launched bike-sharing services. A growing number of studies have been mining bike-sharing data from different cities to understand bike-sharing systems. However, there is still a lack of comprehensive understanding of a bike-sharing network structure. Thus, this paper attempts to use complex network theory and spatial autocorrelation analysis approaches to examine structural properties of bike-sharing systems, quantify the importance of bike stations in the network, and evaluate their spatial clustering patterns. This research analyzes bike-sharing trip data from five different sized bike-sharing systems in the United States, Canada, and China. The results of network analysis reveal that the bike-sharing networks have a small-world property with a small average path length and a high clustering coefficient. Compared with medium-scale networks, the connectivity and accessibility of bike stations are relatively low in the large-scale bike-sharing network. Also, the connectivity of bike stations is positively related to their accessibility while they are not highly correlated with their intermediateness. The spatial clustering analysis results indicate that the spatial distributions of connectivity and accessibility of bike stations have a strong global spatial autocorrelation. Also, the stations with high connectivity and accessibility are concentrated in the urban centers, and the stations with low connectivity and accessibility are clustered in the periphery of the urban areas. However, their intermediateness does not show a strong global spatial clustering pattern, which implies that bike-sharing networks consist of multiple sub-groups. The findings provide new insights for transport planners and managers to understand bike-sharing systems and to improve their service quality. [ABSTRACT FROM AUTHOR]

Mass transit systems as metros thrive nowadays in China – the largest bike-share market all over the world. Hence, it is meaningful to study how a new metro system impact on an existing bike-share system (BS) and understand how they interact to ensure they inter-enhance rather than inter-replace each other. By using trip-pattern and land use to create control and treatment groups based on trip data from Suzhou Public Bicycle System (SPBS), a "before-and-after" study is initiated to measure changes of an existing BS during a period when a new metro system (Here, Metro line 4: M4) opened. Normalized bike flow data (NF) is introduced to cluster and indicate trip-pattern in this study. The result indicates that most SPBS ridership and users within the metro's catchment (the treatment group) have largely increased since the introduction of the M4, while non-commuting cycling increased more than commuting cycling. The exception happens in metros' hub, where SPBS ridership slightly decreased after M4. As for SPBS trip-pattern, before M4 was introduced, trip-pattern in CBD areas in Gusu District is found to be partially imbalanced (high returning in the morning and high renting in the evening), while balanced pattern (similar returning and renting in morning and evening) is more often seen in other areas. After M4 was introduced, a general trip-pattern change from the balanced to the imbalanced is observed regardless of land use, except for educational land use existing in Wuzhong District. This further indicates that after new metros was introduced, SPBS tends to serve "first-mile" interchange (origins to metros) rather than "last-mile" interchange (metros to destinations) in the morning and vice versa in the evening regardless of land use type. This unique pattern could lead to different policy and management in enhancing interchanging service between BS and mass transit. [ABSTRACT FROM AUTHOR]

Abstract This paper discusses the development of shared bike programs in China, especially the innovative dockless bikeshare (DBS) system, using up-to-date empirical analysis. Bicycle sharing programs had existed in China since 2008 but overall bicycle mode share decreased until 2016 when DBS emerged. A comparison of classical city docked bikeshare (BS) programs found that government-oriented operators and a low financial threshold for users were the keys to the success of docked BS in China. In less than two years, a new, innovative, flexible, shared bicycling system – the DBS – has grown from nothing to a substantial 23 million fleets, covering over 200 cities and regions, making docked BS appear insignificant. It is a highly capital-driven, privately-operated business model, largely deployed in cities in conjunction with urban railway systems and has achieved high penetration in mega cites (e.g., 0.135 fleet/resident in Beijing). The development of DBS has experienced "free growth", "regulated" and "limited" phases in a short time. While the central government initially held a " neutral-positive " policy towards this new system, the rapid expansion of dockless fleets soon exceeded cities' limits and resulted in local government policies changing from " neutral-positive " to " neutral-negative ", and from August 2017, forceful limiting regulations have been implemented. DBS systems have advantages such as easy access using a smart phone, convenience of pickup and park and low cost. These merits attract its main users, who are found to be young, highly educated with almost equal numbers of males and females. DBS trips are mainly short, with high frequency and used for commuting purposes. DBS systems have burgeoned due to three factors: (1) those promoting user demands, (2) those winning partial support of government, and (3) those promoting operators' supply. The results show that rapid growth of dockless bikeshare programs is mainly "supply-driven by operators" rather than by "user demand" or "triggered by government policy". Financial sustainability, vandalism and threat to bicycle industry by DBS are the three main challenges that require investigation, especially, the fact that the booming DBS market may cause low profitability for local bicycle manufacturers and thus make the entire industry fragile. Feasibility of docked bikeshare and dockless bikeshare are compared and concluded in the end. [ABSTRACT FROM AUTHOR]

Environmental sustainability is a significant aspect in the sustainable development of modern urban cities, especially in the road transport system. As traffic demands increase, public transport requires more promotion to accommodate the increasing travel demands while maintaining the environmental quality. Public transport, however, is less attractive in vast suburb areas mainly due to its longer travel distance and waiting time. Therefore, this paper proposes a rail-based Park-and-Ride (RPR) scheme to promote public transport in the multimodal transport network. To remedy the heterogeneous distribution of vehicle pollutants in the network, regulations in environmental sensitive districts are required and studied in this paper. To quantitatively evaluate and analyse this joint RPR and environmental regulation strategy in multimodal transport systems, this paper develops an environmental constrained combined modal split and traffic assignment (EC-CMSTA) model. The proposed formulation adopts the concept of fix-point to reformulate the nonlinear complementarity conditions associated with the combined modal split and user equilibrium conditions, which is subsequently incorporated into a VI formulated nonlinear complementarity conditions associated with environmental constraints. The proposed VI formulation can handle a general constraint structure, which enhances the modelling adaptability and flexibility. The strictly monotone and Lipschitz continuity properties of this model are rigorously proved, giving rise to efficient algorithms for the model. A customized projection based self-adaptive gradient projection (SAGP) algorithm is then developed. Numerical studies demonstrate that the EC-MSTA model could enhance the behavioural modelling of network users’ travel decisions and assist in quantitatively evaluating the effectiveness of RPR schemes and environmental regulations. [ABSTRACT FROM AUTHOR]

This paper presents a new perspective for in-station transfer flow estimation, utilising data collected by WiFi sensor system, which is critical for path choice modelling and pedestrian management. The full in-station transfer flow can be estimated by scaling up a 'seed matrix', which is constructed based on the identification of inter-platform transfer activities. Due to sensor failures, the main problem comes from handling the missing elements in the constructed 'seed matrix'. We address this problem with a novel kernel-based framework, named self-measuring multi-task Gaussian process (SM-MTGP). The heterogeneous correlations in temporal features are captured by the designed task-based and input-based kernels separately. Moreover, a self-measuring kernel is designed for learning the correlations carried by the observations. The performance of the proposed method is validated with data from a busy railway station. The results show that the proposed algorithm achieves the best imputation accuracy in both accuracy and robustness, especially at high missing rates. [ABSTRACT FROM AUTHOR]

The purpose of this study was to determine the relationship between bus service satisfaction and the transport mode of choice among university students in Qatar. The degree of bus service satisfaction was collected directly from questionnaire surveys, in which university students were asked questions in relation to their satisfaction with the bus service they used and their transport mode of choice. These questions were categorized into three factors according to confirmatory factor analysis: service at bus stops, service of busses, and service of drivers. Furthermore, the students were asked which mode of transport they used given the choice between public and private transport. This study presents a structural equation model to determine how much bus service satisfaction affects people's decisions about their transport mode. The results from the analysis showed that three key factors-namely, service at bus stops, service of busses, and service of bus drivers-were strongly correlated to the mode of choice. In particular, the bus stop was strongly associated with ease of use, shade, cleanliness, safety, and crowdedness level, while the bus itself influenced reliability, travel time, and frequency. Complying with traffic laws and the driver's attitude were also important contributors to the level of bus service satisfaction. Ultimately, this study will be beneficial for policy/decision-makers. It will allow them to determine what needs to be accomplished to encourage people to use public transportation. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This research focuses on passengers’ satisfaction of the taxi service in Doha, Qatar. Qatar is a rich developing country where taxis are widely considered as a public transportation service due to the limited use of the existing bus service and the profoundly dominant use of private car by expats and Qatari locals. The main objective of this research is to identify the quality attributes of the current taxi service. A descriptive analysis relating the demographic, accessibility, and trip purposes to taxi users in general in Doha with overall service satisfaction was conducted. A Structural Equation Model was used to assess the taxi service attributes that influence the users’ perceived satisfaction. Two models were developed: one was a relationship between the demographic and satisfaction of the users while the other was between the trip time and the satisfaction of the users. The first model supports the premise that income is the greatest attribute in the demographic followed by marital status, age, and occupation. On the other hand, the latter model demonstrated that waiting time is the most contributing variable among the times spent by taxi users followed by walking from work to a station, journey time, and walking from home to a station. [ABSTRACT FROM AUTHOR]

Improving safety at railway level crossings is an important issue for the Australian transport system. Governments, the rail industry and road organisations have tried a variety of countermeasures for many years to improve railway level crossing safety. New types of intelligent transport system (ITS) interventions are now emerging due to the availability and the affordability of technology. These interventions target both actively and passively protected railway level crossings and attempt to address drivers’ errors at railway crossings, which are mainly a failure to detect the crossing or the train and misjudgement of the train approach speed and distance. This study aims to assess the effectiveness of three emerging ITS that the rail industry considers implementing in Australia: a visual in-vehicle ITS, an audio in-vehicle ITS, as well as an on-road flashing beacons intervention. The evaluation was conducted on an advanced driving simulator with 20 participants per trialled technology, each participant driving once without any technology and once with one of the ITS interventions. Every participant drove through a range of active and passive crossings with and without trains approaching. Their speed approach of the crossing, head movements and stopping compliance were measured. Results showed that driver behaviour was changed with the three ITS interventions at passive crossings, while limited effects were found at active crossings, even with reduced visibility. The on-road intervention trialled was unsuccessful in improving driver behaviour; the audio and visual ITS improved driver behaviour when a train was approaching. A trend toward worsening driver behaviour with the visual ITS was observed when no trains were approaching. This trend was not observed for the audio ITS intervention, which appears to be the ITS intervention with the highest potential for improving safety at passive crossings. [ABSTRACT FROM AUTHOR]

Abstract: The system considered is a cinema ticketing booth system. A general simulation algorithm is presented as well as the system’s operating characteristics. The results of the experiment were verified by comparing them with video observation data and theoretical values. Finally, with comparative analysis of experiment data, the developed simulation model was able to replicate the situation in which pedestrians find an available booth to occupy while waiting in a queue. The model can facilitate the availability of various pedestrian flows and a range of operating times. With some efforts of computer programming, the situations where multiple booths are available were simulated to identify pedestrian movement. The developed simulation model captures important details, such as travel time, wait time, queue length and the number of waiting pedestrians with the different number of pedestrian flows and booths. The paper presents a means to designing the pedestrian operation and plan on the basis of the estimated number of people. [Copyright &y& Elsevier]

In recent years, many cities have implemented bike-sharing programs (BSP) to improve the travel efficiency of short trips. Early studies have analyzed how built environment factors affected bike-sharing usage. However, these studies mainly used global regression models that cannot demonstrate the spatial variation relationship between the built environment and bike usage. Therefore, this study employs both a global regression model and a geographically weighted regression (GWR) model to examine the global and local influences of the built environment on bike usage, which represents the average bike trips on workdays and non-workdays. This research takes Suzhou, China as a case study area. It uses one-year bike-sharing trip data, metro ridership data, cycling infrastructure data, cellular signaling data, and points of interest (POI) data. The global regression results show that bike stations near public transit, restaurants, shopping malls, and educational and financial places have high numbers of bike trips on both workdays and non-workdays; however, bike station proximity to workplaces is positively associated with bike trips on workdays but not on non-workdays. The results of GWR are partially consistent with the global regression results and show the local effects of the built environment on bike usage in different parts of Suzhou. Also, the goodness of fit in the GWR is better than that of the global regression model. The findings of this study provide strategic guidance to improve the service quality of bike-sharing systems as there is a pressing need to integrate BSP policies into the land use planning framework to encourage more diverse transport modal change and incentivize more commuting. • This study examines the global and local effects of the built environment on bike usage. • The effects of the built environment on bike usage are diverse in different areas of Suzhou. • In some areas surrounding the urban center, built environment factors have a positive impact on bike usage. • This study draws on a policy of land use-transportation coordination to make the best use of shared bikes. [ABSTRACT FROM AUTHOR]

For station-based bike-sharing systems, the balance between user demand and bike allocation is critical for the operation. As a basic operational index, the short-term prediction of bike numbers (flow) plays an important role in demand forecasting and rebalancing resources of bike-sharing networks. Many different methods have been proposed for bike forecast in recent years, and the deep learning (DL)-based models have dominated this area because of their competitive performance. However, there still exist challenges in such approaches including: (i) how to appropriately select the training input for the DL model, and (ii) how to effectively utilize both the temporal and spatial features in the data for prediction. In particular, the arbitrary input may limit the model optimization, and the separate consideration of temporal and spatial features could change the original data representation. This paper uses a simple autocorrelation function to select the best input candidates and develops a three-dimensional (3 D) residual neural network to learn spatiotemporal features simultaneously. The proposed DL model is trained and validated using two separate bike-sharing datasets from New York and Suzhou cities. The learned features by two-dimensional (2 D) and 3 D CNN kernels under different input methods are compared. Results show that 3 D CNN outperforms other models and that the proposed input selection method yields better learning results for both datasets. The proposed methods help with a comprehensive DL model workflow and better forecasting accuracy. [ABSTRACT FROM AUTHOR]

While the development of cities tends to focus on improving traffic mobility, it has gradually neglected people's demand for safety and comfort walking on the streets. To address this problem, shared streets that can integrate traditional street life and traffic mobility are getting more attention as pedestrian-friendly development. In order to measure the performance of shared streets, it is essential to identify how people feel when driving and walking around. However, investigating the various factors that influence the real world is not straightforward because of cost, time-consuming, and safety problems. Virtual reality and the Human-in-the-loop (HITL) have become valuable tools for conducting experiments without compromising them. The experiments are performed on both pedestrians' and drivers' sides. The three shared street layouts in a virtual environment are designed according to Europe's real shared street cases. To evaluate shared street effects, questions in five aspects: amenity, walking or driving experience, safety, economy or priority, and environmental perception are asked to participants, respectively. MPR, EWM, and Fuzzy Comprehension Evaluation methods are used to assess the performance. The result revealed that different groups of people have different sensitivity and preferences for each evaluation criteria. However, the results of the comprehensive evalutation showed that scenario C with the largest isolation measurement is preferable in both pedestrian and driver's groups based on shared street design elements. The city planners can get help from this shared street analysis, where the new design and layout could be tested in advance. [ABSTRACT FROM AUTHOR]

• Investigated the impact of real-time pedestrian scale info on driver braking using a VR driving simulator and eye-tracking. • Created and evaluated a novel warning system that provides drivers with dynamic information about pedestrian crowd size. • Showed that crowd scale info significantly affects drivers' deceleration, yielding behavior, and attention levels. • Found that larger pedestrian groups increase Time to Collision (TTC) and stopping distances, enhancing safety. • Proved the system's effectiveness in complex road conditions, especially under low visibility, regardless of driver gender. A driver warning system can improve pedestrian safety by providing drivers with alerts about potential hazards. Most driver warning systems have primarily focused on detecting the presence of pedestrians, without considering other factors, such as the pedestrian's gender and speed, and whether pedestrians are carrying luggage, that can affect driver braking behavior. Therefore, this study aims to investigate how driver braking behavior changes based on the information about the number of pedestrians in a crowd and examine if a developed warning system based on this information can induce safe braking behavior. For this purpose, an experiment scenario was conducted using a virtual reality-based driving simulator and an eye tracker. The collected driver data were analyzed using mixed ANOVA to derive meaningful conclusions. The research findings indicate that providing information about the number of pedestrians in a crowd has a positive impact on driver braking behavior, including deceleration, yielding intention, and attention. Particularly, It was found that in scenarios with a larger number of pedestrians, the Time to Collision (TTC) and distance to the crosswalk were increased by 12%, and the pupil diameter was increased by 9%. This research also verified the applicability of the proposed warning system in complex road environments, especially under conditions with poor visibility such as nighttime. The system was able to induce safe braking behavior even at night and exhibited consistent performance regardless of gender. In conclusion, considering various factors that influence driver behavior, this research provides a comprehensive understanding of the potential and effectiveness of a driver warning system based on information about the number of pedestrians in a crowd in complex road environments. [ABSTRACT FROM AUTHOR]

• The study examines the influence of factors on injury severity outcomes of elderly pedestrians across various road segments. • Temporal instability of factor impacts were evaluated across three periods: pre-COVID-19, during COVID-19, and post-COVID-19. • A binary logit model with mean heterogeneity was utilized to analyze the crash data. • Findings indicated significant variations in factor impacts across different road segments and timeframes. • Head injuries notably increased the likelihood of fatal or severe injuries, particularly at non-intersections during the COVID-19 pandemic. This study examines the variability in the impacts of factors influencing injury severity outcomes of elderly pedestrians (age >64) involved in vehicular crashes at intersections and non-intersections before, during, and after the COVID-19 pandemic. To account for unobserved heterogeneity in the crash data, a random parameters logit model with heterogeneity in the means approach is utilized to analyze vehicle-elderly pedestrian crash data from Seoul, South Korea, occurring between 2018 and 2022. Preliminary transferability tests revealed instability in factor impacts on injury severity outcomes, highlighting the need to estimate individual models across various road segments and time periods. Thus, the dataset was segregated by crash location (intersection/non-intersection) and period (before, during, and after COVID-19), with individual models estimated for each group. Results obtained from the analyses revealed that back injuries positively influenced fatalities at non-intersections after the pandemic and was negatively associated with fatalities at intersections before the pandemic. Additionally, several indicators demonstrated significant instability in their impact magnitudes across different road segments and crash years. During the pandemic, head injuries increased the probability of fatalities higher at non-intersections. After the pandemic, crosswalk locations decreased the possibility of fatalities more at intersections. Compared to intersection segments, the female indicator reduced the likelihood of fatal injuries at non-intersections more before, during, and after the pandemic. Before the pandemic, much older pedestrians experienced a greater decline in fatalities at intersections than non-intersections. This instability could be attributed to altered mobility patterns stemming from the COVID-19 pandemic. Overall, the study findings highlight the variability of determinants of fatal/severe injury outcomes among elderly pedestrians across various road segments and years, with the underlying cause of this fluctuation remaining unclear. Furthermore, the findings revealed that accounting for heterogeneity in the means of random parameters enhances model fit and provides valuable insights for safety professionals. The factor impact variability in the estimated models carries significant implications for elderly pedestrian safety, especially in scenarios where precise projections of the effects of alternative safety measures are essential. Road safety experts can leverage these findings to refine or update current policies to enhance elderly pedestrian safety at intersections and non-intersections. [ABSTRACT FROM AUTHOR]

Models based on neural networks (NN) have been used widely and successfully in traffic prediction resulting in improved accuracy and efficiency in traffic flow, speed, passenger flow, and delay. Input data include continuous and discrete variables and these impact traffic changes both internally and externally. However, few studies have focused on discrete traffic-related variables in NN-based forecasting models. Inappropriate utilization of discrete variables may cause useful factors to become insignificant and lead to an inefficient forecasting model. In this paper, a NN-based model is used to predict traffic flow of a bike-sharing system in Suzhou, China. The model only uses external and discrete variables like weather, places of interest (POIs), and holiday periods. We applied both entity embedding and one-hot encoding for the data preprocessing of these variables. The results show that (1) Entity embedding can effectively increase the continuity of categorical variables and slightly improve the prediction efficiency for the NN model; and (2) The hidden relationship in variables can be identified through visual analysis, and the trained embedding vectors can also be used in traffic-related tasks. [ABSTRACT FROM AUTHOR]

Neural network (NN)-based models have recently achieved outstanding results in short-term traffic prediction. However, most of these are based on the regression approach and trained to generate a single data point as a predicted value for future timesteps, which does not provide information on prediction uncertainty and limits its performance under different traffic conditions. To solve this problem, this study proposes a novel, high-dimensional distributional prediction (HDP) framework. This method has been validated by a series of experiments using the Caltrans Performance Measurement System dataset and four widely used NN models. The results suggest that the proposed HDP scheme can help existing NN structures to (1) generate adaptive distributional predictions for quantifying the uncertainty of multiple targets, and (2) gain better point prediction in terms of accuracy and robustness. Furthermore, we demonstrate that predicted speed distributions can be used for travel time estimation, outperforming other traditional methods in unexpected traffic conditions such as traffic incidents. [ABSTRACT FROM AUTHOR]

This paper designs a refueling system for battery electric buses (BEBs) by applying battery-swapping technologies together with a local charging system. The service capability of battery-swapping stations (BSSs) is restricted by the number of installed swapping robots. Depleted batteries are charged at BSSs which is equipped with a local charging system having a number of standby batteries and chargers in various types. This study intends to answer four fundamental questions: How many BSSs should be installed? Where should they be? How to assign the depleted electric buses to BSSs? What is the service capability of the BSS? An integer linear program is first formulated to represent the problem with deterministic demand. A two-stage stochastic programing model is then formulated to handle the demand uncertainties, which is then solved by a customized gradient algorithm. The test on a real network of the southeast region of Melbourne in Australia verifies the feasibility of the proposed model and draws managerial insights for the location and configuration of BSSs. [ABSTRACT FROM AUTHOR]

Highlights • A spatial clustering method for macro-level traffic crash analysis. • Kernel density estimation with spatial clustering. • Evaluate the spatial influence of point-of-interest (POI) on traffic crashes. • Clustering and non-clustering approach are compared by OLS, SEM, SLM. Abstract This paper presents a spatial clustering method for macro-level traffic crash analysis based on open source point-of-interest (POI) data. Traffic crashes are discrete and non-negative events for short-time evaluation but can be spatially correlated with long-term macro-level estimation. Thus, the method requires the evaluation of parameters that reflect spatial properties and correlation to identify the distribution of traffic crash frequency. A POI database from an open source website is used to describe the specific land use factors which spatially correlate to macro level traffic crash distribution. This paper proposes a method using kernel density estimation (KDE) with spatial clustering to evaluate POI data for land use features and estimates a simple regression model and two spatial regression models for Suzhou Industrial Park (SIP), China. The performance of spatial regression models proves that the spatial clustering method can explain the macro distribution of traffic crashes effectively using POI data. The results show that residential density, and bank and hospital POIs have significant positive impacts on traffic crashes, whereas, stores, restaurants, and entertainment venues are found to be irrelevant for traffic crashes, which indicate densely populated areas for public services may enhance traffic risks. [ABSTRACT FROM AUTHOR]

Predicting the risk of maritime accidents is crucial for improving traffic surveillance and marine safety. The availability of data sources and development of machine learning and deep learning methodologies can improve operational risk prediction. Similar to larger vessels, numerous ocean accidents are caused by small- and medium-sized vessels owing to poor conditions and defective equipment. This study aims at investigating the application of deep learning in both short- and long-term predictions of different types of accident risks associated with small vessels by considering multiple influencing factors. Herein, several big data sources that contain data collected from the territorial sea of South Korea, including ocean accidents, ocean depth, weather data, and small vessel trajectories, are utilized. Four machine learning and six deep learning algorithms are implemented and compared in nine scenarios with three different grid sizes using daily, weekly, and monthly models. The results reveal that although the performance of the proposed deep spatiotemporal ocean accident prediction (DSTOAP) model varies according to grid sizes and time intervals, its accuracy (more than 78%) makes it reliable for predicting accidents. Furthermore, although all types of accidents are captured with high accuracy, more than 84% of collision accidents can be predicted accurately. For practical applications, the results of this study can guide ocean accident management and safety planners in choosing appropriate methods for different time schedules and grid sizes, according to the range of coverage of the patrol ship. • Real-world big dataset considering different long- and short-term time steps. • All types of accidents can be predicted, regardless of the sparsity of accident data. • A Deep Learning Model has been proposed to capture both spatial and temporal features. • The changing environmental conditions of oceans are overcome by applying a spatial model. [ABSTRACT FROM AUTHOR]

This paper focuses on the safety issue for cyclists and pedestrians at unsignalized intersections. The cycling speed needs to be calmed when approaching the intersection. This study proposes and deploys an integrated portable ultra-wideband/inertial navigation system (UWB/INS) to extract cycling trajectories for a cycling safety study. The system is based on open-source hardware and delivers an open-source code for an adaptive Kalman filter to enhance positioning precision for data quality assurance at an outdoor experimental site. The results demonstrate that the system can deliver reliable trajectories for low-mobility objects. To mitigate accident risk and severity, varied cycling speed calming measures are tested at an experimental site. Based on the trajectory data, the statistical features of cycling velocities are evaluated and compared. A new proposed geometric design is found to be most effective when compared with conventional traffic signs. [ABSTRACT FROM AUTHOR]

The satisfaction of road users is one of the main goals of any highway authority. This study evaluates and identifies factors that affect users' satisfaction with the road network. A questionnaire was used to collect the data in this study. The survey questions were categorized into four latent variables through exploratory factor analysis and confirmatory factor analysis: road conditions with respect to drive, safety management, road conditions while ongoing construction, and road user satisfaction. Structural equation modeling was used to explore the satisfaction of users while accounting for latent constructs affecting their behavior. The results showed that factors related to safety management and policies regarding construction activities were significantly related to users' satisfaction. These findings indicate that as long as traffic safety and disruptions are managed appropriately, road users are satisfied with their driving experience. Several strategies were recommended to improve these factors. The method described in the study can provide highway authorities and public agencies with a useful guideline to identify factors that affect road users' satisfaction. [ABSTRACT FROM AUTHOR]

The hook turn, which is rarely seen outside of Melbourne, Australia, reduces congestion in narrow road spaces shared with trams. Australia allows people from 44 nations to convert their home country driver's license to an Australian driver's license without a driving test. Visitors who have never heard of the hook-turn experience difficulty driving following the new traffic rule. From this aspect, investigating how inexperienced drivers encounter the hook-turn intersection is valuable for safety reasons. A driving simulator including virtual reality technology is developed to evaluate the level of safety of human driving behavior. The simulator in this research was developed by integrating Vissim and Unity3D embedded head-mounted display and driving devices to ensure a better driving experience. This research presented the development of a robust virtual reality driving simulator. It investigated how nonexperienced drivers respond to a completely new road condition. The results were compared with microsimulation outcomes (here, Vissim). The results showed that a human-driven car had a higher collision risk than a computer-driven car. The trajectories of the driver type were statistically different (t = 6.03, p 0.01, in the case of time-to collision ≤1.5 between experienced and computerized drivers). Participant responses to a postexperiment survey found that the simulator was realistic (4.31 out of 5.00), which could help beginner drivers (4.00 out of 5.00). Therefore, the simulator can be utilized for safety-related research as well as drivers' training. [ABSTRACT FROM AUTHOR]

• Factors associated with clusters of personal mobility rider-at-fault crashes are explored. • A two-stage cluster-rule mining approach is utilized for data clustering and pattern mining. • Key patterns within identified clusters are identified based on the type of crash partner. • Speeding, nighttime, and side collisions are linked to fatal/severe rider at-fault crashes involving other vehicles. • Fatal/severe rider at-fault crashes with pedestrians are associated with pedestrian crossings and traffic rule violations. Personal Mobility Devices (PMDs) have witnessed an extraordinary surge in popularity, emerging as a favored mode of urban transportation. This has sparked significant safety concerns, paralleled by a stark increase in PMD-involved crashes. Research indicates that PMD user behavior, especially in urban areas, is crucial in these crashes, underscoring the need for an extensive investigation into key factors, particularly those causing fatal/severe outcomes. Remarkably, there exists a noticeable gap in the research concerning the analysis of determinants behind fatal/severe PMD crashes, specifically in PMD rider-at-fault collisions. This study addresses this gap by identifying uniform groups of PMD rider-at-fault crashes and investigating cluster-specific key factor associations and determinants of fatal/severe crash outcomes using Seoul's PMD rider-at-fault crash data from 2017 to 2021. A comprehensive two-step framework, integrating Cluster Correspondence Analysis (CCA) and Association Rules Mining (ARM) techniques is employed to segment PMD rider-at-fault crash data into homogeneous groups, revealing unique risk factor patterns within each cluster and further exploring the combination of factors associated with fatal/severe PMD rider-at-fault crash outcomes. CCA revealed three distinct groups: PMD-vehicle, PMD-pedestrian, and single-PMD crashes. From the ARM, it was found that fatal/severe crashes were linked to dry road conditions, male PMD users, and weekdays, irrespective of the cluster. Whereas speeding violations and side collisions were associated with fatal/severe PMD-vehicle rider-at-fault crashes, traffic control violations were related to fatal/severe PMD-pedestrian rider-at-fault crashes at pedestrian crossings. Unsafe riding practices predominantly caused single-PMD crashes during daytime hours. From the findings, engineering improvements, awareness campaigns, education, and law enforcement actions are recommended. The new insights gleaned from this research provide a foundation for informed decision-making and the implementation of policies designed to enhance PMD safety. [ABSTRACT FROM AUTHOR]

In recent years, many cities around the world have implemented bike-sharing programs. A number of studies on the relationship between the built environment and bike usage have provided important insights into understanding bike-sharing systems. However, the effects of the built environment on the structural properties of bike-sharing networks are seldom discussed in the literature. This research proposes a novel and interdisciplinary framework to explore how built environment factors affect the topological properties of bike-sharing networks. Firstly, this research applies a complex network approach to quantify the importance of bike stations in the network. Then, multisource data are utilized to identify comprehensive built environment attributes. Finally, spatial regression models are used to reveal the relationship between the importance of bike stations and built environment. In this study, the bike-sharing system in Suzhou, China, is taken as a case study. The empirical result shows that the importance of bike stations displays strong spatial dependence. Also, built environment attributes such as resident population, accessibility to subway stations, the capacity of bike stations, and the total length of main roads within a catchment area have different effects on the importance of bike stations. It should be noted that the floating population and the number of bus stops surrounding bike stations do not have strong correlations with the importance of bike stations. The findings of this study can guide urban planners and operators to improve the service quality and resilience of bike-sharing systems. [ABSTRACT FROM AUTHOR]

The study investigated factors affecting the severity of red-light-running (RLR) -related crashes by means of crash records from the State of Florida (US). Previous studies have attempted to incorporate road, driver, and environmental factors into ordinal regression models to predict the severity of crashes. However, investigating the relationship among variables can become a difficult task with traditional statistical techniques. Some explanatory variables, in fact, may impact crash severity indirectly, through one or more mediating variables (measured or unobserved). This study employed structural equation modeling (SEM) to explore the indirect relationship between crash severity and its contributing factors. SEM is a statistical technique able to account for unobserved variables, whereas traditional severity models have focused on measured variables only. Three unobserved variables were proposed in this study to better describe the dynamic of RLR-related crashes: precrash travel speed (TS) of the bullet vehicle (at fault), the kinetic energy (KEs) applied from the bullet vehicle to the subject vehicle(s), and crash severity (overall energy resulting from a crash). A SEM model was developed to estimate the hypothesized relationships among speed, kinetic energy, and crash severity. Measured (observed) variables obtained from crash records were included in the SEM model to define latent dimensions. The results showed that crash data supported the model hypothesis and measured/unobserved variables adequately predicted crash severity. Overall, speed and kinetic energy were demonstrated to positively affect crash severity with kinetic energy being the more influential factor. Moreover, it was demonstrated that the most influential factor with respect to TS was alcohol-impaired driving conditions and the least influential was age of the at-fault driver. Regarding KEs, the number of vehicles involved in the crash showed the most influence and vehicle year was the least influential factor. [ABSTRACT FROM AUTHOR]

• This research aimed to investigate the prevalence and factors associated with turn signal use at intersections. • Turn signal use among car drivers (68.27%) was found to be significantly higher than motorcyclists (40.13%). • The findings show the need for greater targeted enforcement of existing legislation and extensive road safety education. Turn signal neglect is a major cause of traffic crashes, particularly rear-end collisions. However, little research has investigated the use of turn signals among motorists, particularly in developing countries with high levels of motorcycle use. This research aimed to investigate the prevalence and factors associated with turn signal use at intersections among car drivers and motorcyclists in Da Nang, Vietnam. Cross-sectional roadside observations were undertaken at 24 sites across Da Nang City during weekday and weekend periods. A total of 17,142 vehicles were observed, including 2392 cars and 14,750 motorcycles. Turn signal use among car drivers (68.27%) was found to be significantly higher than motorcyclists (40.13%). Binary logistic regression modelling showed that turn signal neglect at intersections was associated with making a right turn, not carrying passengers, travelling outside of the city centre, travelling on weekdays, and the absence of separate car lanes, pedestrian crossings and traffic lights. Despite national legislation regulating turn signal use in Vietnam, the use of turn signals is relatively low compared with developed countries. The findings highlight the need for both greater and more targeted enforcement of existing legislation combined with extensive road safety education. [ABSTRACT FROM AUTHOR]

Abstract Bus services can be seen as a way to reduce traffic congestion where they can encourage a mode shift from car. However, they can also generate negative effects on traffic flow due to stop-start operations at bus stops. This paper aims to assess the net impact of bus operations on traffic congestion in Melbourne. The methodology used to achieve this aim comprised of three main stages. First, a primary survey was conducted to determine the mode shift from bus to car when buses are unavailable. This figure was used to estimate the positive impact of buses on relieving congestion. Second, the negative impact of buses was investigated by considering the effect of bus stop operations on vehicle traffic flow using microsimulation. Finally, the net effect was estimated by contrasting congestion measures determined from a traditional four step model between two scenarios: ‘with bus’ and ‘without bus’. The results indicated that Melbourne’s bus network contributes to reduce the number of severely congested road links by approximately 10% and total delay on the road network by around 3%. The highest congestion relief impact was found in inner Melbourne with a 7% decrease in vehicle time travelled and total delay, and 16% decrease in the number of heavily congested road links. In inner areas, the level of congestion is relatively high so the mode shift from car to bus, even if not as high as middle and outer areas, have a significant effect on relieving traffic congestion. Areas for future research are suggested such as investigating the long-term effect of buses on traffic congestion. [ABSTRACT FROM AUTHOR]

Highlights • Methodological framework for a new concept, remote park-and-ride scheme. • Cross-nested logit model for combined modal split and traffic assignment. • Extension to the case with asymmetric and non-additive travel time functions. • Model for the multimodal stochastic system optimum. • Exact method for the optimal network design based on nonlinear valid inequalities. Abstract Existing park-and-ride (P&R) sites are usually located near a train/bus station where construction and operation costs are considerably high. Thus, this paper proposes a new P&R service mode, "Remote P&R (RPR)", where the car park locates in a suburban area with lower land value. Dedicated express bus service is used to connect this site and a nearby train station. To quantitatively evaluate the impacts of RPR on the network flows, a combined modal split and traffic assignment model (CMSTA) is developed, where a cross-nested logit (CNL) model is adopted to cope with the mode similarity. The problem is formulated as a convex programming model and solved by the Evans algorithm, and then extended to asymmetric path-based cases, where a variational inequality (VI) model is built and solved by a self-adaptive gradient projection (SAGP) algorithm. Taking the CMSTA as the lower level and multimodal stochastic system optimum (MSSO) as the objective, we further develop a mathematical programming model with equilibrium constraints (MPEC) for the optimal network design of RPR. Based on an origin-based reformulation of the MPEC model, an exact solution method based on the nonlinear valid inequalities (NVI) is applied. Numerical examples demonstrate that the RPR services can significantly influence network users' travel decisions, promote the usage of public transportation and mitigate traffic congestion in the congested area of metropolitan cities. [ABSTRACT FROM AUTHOR]

The path choice behavior of battery electric vehicle (BEV) drivers is influenced by the lack of public charging stations, limited battery capacity, range anxiety and long battery charging time. This paper investigates the congestion/flow pattern captured by stochastic user equilibrium (SUE) traffic assignment problem in transportation networks with BEVs, where the BEV paths are restricted by their battery capacities. The BEV energy consumption is assumed to be a linear function of path length and path travel time, which addresses both path distance limit problem and road congestion effect. A mathematical programming model is proposed for the path-based SUE traffic assignment where the path cost is the sum of the corresponding link costs and a path specific out-of-energy penalty. We then apply the convergent Lagrangian dual method to transform the original problem into a concave maximization problem and develop a customized gradient projection algorithm to solve it. A column generation procedure is incorporated to generate the path set. Finally, two numerical examples are presented to demonstrate the applicability of the proposed model and the solution algorithm. [ABSTRACT FROM AUTHOR]

Network area-wide impacts due to major traffic incidents can be assessed using a microsimulation approach. A VISSIM microsimulation model for a motorway network has been developed and is used to quantify impacts of a major incident in terms of associated costs. The modelled results reveal that a 65% capacity reduction results in 36% more incident-induced delay when compared with the application of a 50% capacity reduction assumption for a two-hour incident clearance duration that blocked one lane of a two-lane motorway. Additionally, an incident which caused a full blockage incurred 40 times more associated impact costs when compared with a major incident which caused a one lane blockage. A 23% cost saving can be achieved by clearing one lane of a fully blocked two-hour major traffic incident after 90 minutes, while a 37% cost saving can be achieved by clearing all blockages after 90 minutes. [ABSTRACT FROM AUTHOR]

[Display omitted] • Applications of fMRI, EEG, fNIRS and MEG in road safety and driving behaviour research analysed. • (Dis)advantages of each neuroimaging method in various driving behaviour experiments discussed. • EEG found to be the most popular neuroimaging method in driving experiments, followed by fMRI. • fMRI studies mostly experimented neural effects of recreational drugs and distractive stimuli. • EEG experiments predominantly focused on driver fatigue detection and sleepiness. Applications of neuroimaging methods have substantially contributed to the scientific understanding of human factors during driving by providing a deeper insight into the neuro-cognitive aspects of driver brain. This has been achieved by conducting simulated (and occasionally, field) driving experiments while collecting driver brain signals of various types. Here, this sector of studies is comprehensively reviewed at both macro and micro scales. At the macro scale, bibliometric aspects of these studies are analysed. At the micro scale, different themes of neuroimaging driving behaviour research are identified and the findings within each theme are synthesised. The surveyed literature has reported on applications of four major brain imaging methods. These include Functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS) and Magnetoencephalography (MEG), with the first two being the most common methods in this domain. While collecting driver fMRI signal has been particularly instrumental in studying neural correlates of intoxicated driving (e.g. alcohol or cannabis) or distracted driving, the EEG method has been predominantly utilised in relation to the efforts aiming at development of automatic fatigue/drowsiness detection systems, a topic to which the literature on neuro-ergonomics of driving particularly has shown a spike of interest within the last few years. The survey also reveals that topics such as driver brain activity in semi-automated settings or neural activity of drivers with brain injuries or chronic neurological conditions have by contrast been investigated to a very limited extent. Potential topics in driving behaviour research are identified that could benefit from the adoption of neuroimaging methods in future studies. In terms of practicality, while fMRI and MEG experiments have proven rather invasive and technologically challenging for adoption in driving behaviour research, EEG and fNIRS applications have been more diverse. They have even been tested beyond simulated driving settings, in field driving experiments. Advantages and limitations of each of these four neuroimaging methods in the context of driving behaviour experiments are outlined in the paper. [ABSTRACT FROM AUTHOR]